Multi-scale Segmentation Method Research Articles

A multi-order FRFT self-adaptive filter based on segmental frequency fitting and early fault diagnosis in gears

Abstract To effectively diagnose gear failure at an early stage, a multi-order Fractional Fourier transform (FRFT) self-adaptive filter based on segmental frequency fitting (MFSFF) is proposed to separate the feature components with curved frequency from the gearbox’s transient conditions. First, a linear multi-scale segmentation method (LMSS) is developed to divide the signal with curved frequency into segments with nearly linear frequency; then, a method for determining the FRFT filter parameters by fitting the frequency curve (DFFPFF) is developed to calculate the FRFT filter parameters for each signal segment, and the signal in each segment is filtered by an FRFT filter using these parameters to determine the MFSFF. The vibration of the gearbox’s acceleration and deceleration process is analyzed using an MFSFF and the filtered signal is demodulated. The experimental results show that LMSS is able to divide any signal with curved frequency into minimal segments with nearly linear frequency; DFFPFF is exact, fast, not influenced by the vibration source or the number of components, and able to determine the FRFT filter parameters for each signal segment accurately; the feature component of the gearbox’s transient condition is accurately extracted by an MFSFF, and the other components and noise are removed simultaneously. Early gear failure is diagnosed exactly by demodulation of the extracted feature component, which is difficult to identify using the traditional method.

Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.

Using the estimation of scale parameters (ESP) image segmentation tool to determine the ideal image segmentation scale, the optimal segmented image was created by the multi-scale segmentation method. Based on the visible vegetation indices derived from mini-UAV imaging data, we chose a set of optimal vegetation indices from a series of visible vegetation indices, and built up a decision tree rule. A membership function was used to automatically classify the study area and an aquatic vegetation map was generated. The results showed the overall accuracy of image classification using the supervised classification was 53.7%, and the overall accuracy of object-oriented image analysis (OBIA) was 91.7%. Compared with pixel-based supervised classification method, the OBIA method improved significantly the image classification result and further increased the accuracy of extracting the aquatic vegetation. The Kappa value of supervised classification was 0.4, and the Kappa value based OBIA was 0.9. The experimental results demonstrated that using visible vegetation indices derived from the mini-UAV data and OBIA method extracting the aquatic vegetation developed in this study was feasible and could be applied in other physically similar areas.

FDR-control in multiscale change-point segmentation

Fast multiple change-point segmentation methods, which additionally provide faithful statistical statements on the number, locations and sizes of the segments, have recently received great attention. In this paper, we propose a multiscale segmentation method, FDRSeg, which controls the false discovery rate (FDR) in the sense that the number of false jumps is bounded linearly by the number of true jumps. In this way, it adapts the detection power to the number of true jumps. We prove a non-asymptotic upper bound for its FDR in a Gaussian setting, which allows to calibrate the only parameter of FDRSeg properly. Change-point locations, as well as the signal, are shown to be estimated in a uniform sense at optimal minimax convergence rates up to a log-factor. The latter is w.r.t. $L^p$-risk, $p \ge 1$, over classes of step functions with bounded jump sizes and either bounded, or possibly increasing, number of change-points. FDRSeg can be efficiently computed by an accelerated dynamic program; its computational complexity is shown to be linear in the number of observations when there are many change-points. The performance of the proposed method is examined by comparisons with some state of the art methods on both simulated and real datasets. An R-package is available online.

CLASSIFICATION OF URBAN FEATURE FROM UNMANNED AERIAL VEHICLE IMAGES USING GASVM INTEGRATION AND MULTI-SCALE SEGMENTATION

Abstract. The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

Development of a multi-scale object-based shadow detection method for high spatial resolution image

Effective treatment of shadows generated by the obstruction of trees and buildings is an inevitable task for extracting detailed spectral and spatial information from urban high-resolution images. Object-based shadow detection methods can take full advantages of spatial features in the urban very high resolution (VHR) images. However, the effect of different segmentation parameters for detecting shadows has not been well studied. In this study, we proposed an object-based method for shadow detection on urban high-resolution image and addressed quantitative assessment of segmentation. In proposed object-based method, a multi-scale segmentation method, known as fractal net evolution approach (FNEA), was employed to generate primitive objects; then, three spectral properties of shadows were fused based on Dempster–Shafer (D–S) evidence theory to identify shadows. In quantitative assessment, a method for ordering significance of parameters and deriving optimal parameters based on orthogonal experimental design was proposed to evaluate the impact of different segmentation variables on the accuracy of shadow detection. Experimental results indicate that the best overall accuracy (OA) for shadow detection of our method was 89.60% after segmentation parameters’ optimization and scale is the most influential parameter of FNEA segmentation parameters in determining the performance of shadow detection.

A novel registration method for high resolution remote sensing images based on JSEG and NMI

When traditional multi-scale analysis tools are applied to high resolution remote sensing image registration, difficulties and limitations are common in selection of directional sub-bands and distribution optimization of control point pairs etc. Aiming at this issue, a novel registration method based on JSEG and NMI is proposed in this paper. It is the method that incorporates the multi-scale segmentation method (JSEG) into image registration for the first time and proposes an adaptive feature point extraction method on the basis of blocking strategy. Then, NMI is adopted to obtain a set of control point pairs. Finally, the image registration is realized by virtue of Delaunay triangle local transform mapping functions. In accordance with experiments on high resolution remote sensing images collected by different sensors, it is found that the method can not only extract feature points accurately but also ensure reasonable distribution of control point pairs. Meanwhile, compared with traditional multi-scale tools-based methods, the method has relatively high accuracy and robustness.

Multi-scale salient region and relevant visual keywords based model for automatic image annotation

Automatic image annotation is a vital and challenging problem in pattern recognition and image understanding areas. The existing models directly extract visual features from segmented image regions. Since segmented image regions may still have multi-objects, the extractive visual features may not effectively describe corresponding regions. In addition, existing models did not consider the visual representations of corresponding keywords, which would lead to appearing plenty of irrelevant annotations in final annotation results, and these annotations did not relate to any part of images considering visual contents. In order to overcome the above problems, an image annotation model based on multi-scale salient region and relevant visual keywords is proposed. In this model, each image is segmented by using multi-scale grid segmentation method and the global contrast based method is used to extract the saliency maps from each image region. Visual features are extracted from each salient region. In addition, each keyword is divided into two categories: abstract words or non-abstract words. Visual seeds of each non-abstract word are established, and then a new method is proposed to extract visual keyword collections by using corresponding seeds. According to the traits of abstract words, an algorithm based on subtraction regions is proposed to extract visual seeds and corresponding visual keyword collections of each abstract word. Adaptive parameter method and a fast solution algorithm are proposed to determine the similarity thresholds of each keyword. Finally, multi-scale visual features and the combinations of the above methods are used to improve the annotation performance. Our model can improve the object descriptions of images and image regions. Experimental results verify the effectiveness of the proposed model.

Multi-scale and shape constrained localized region-based active contour segmentation of uterine fibroid ultrasound images in HIFU therapy.

PurposeTo overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.MethodsWe incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.ResultsExperimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).ConclusionsAn accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.

Object-oriented mapping application of architecture based on multi-scale image segmentation and image extraction

Abstract. Residential area detection is extremely important for supervision of rush-built and illegal construction behavior to protect arable land and basic farmland. Traditional manual recognition of illegal construction is simple but time-consuming. The specific research work in the thesis includes: the proposition and designing of a multi-scale segmentation method based on watershed segmentation combined with region merging algorithm, comparative experiments of the new algorithm and traditional algorithm. Then, architecture extraction experiment based on the multi-scale segmentation method is carried out and analysis of the precision of comparative experiment results of pixel-based and object-oriented extraction methods is made, all these steps facilitate the automatic mapping of the architecture.

A Multi-scale Segmentation Method of Oil Spills in SAR Images Based on JSEG and Spectral Clustering

Image segmentation is a key step of oil spills detection in SAR images. For the problem that the traditional multi-spectral clustering algorithm with the features extraction by GLCM (Gray-Level Co-occurrence Matrix) has such limitations as direction sensitivities and difficulties in selecting the best feature combination etc., this paper proposes a multi-scale segmentation method of oil spills in SAR images based on JSEG and spectral clustering. Multi-scale J-images are used to extract the multi-features and the Laplace matrix is clustered by the K-means method. Finally, a decision-level fusion strategy is used to fuse the segmentation results from different scales. Two sets of experiments show that, compared to the traditional spectral clustering methods based on the gray feature and multi-textual features, the proposed method has higher accuracy and stronger robustness.

Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET.

Combined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications. Our method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering schemeis to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image. This method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2%±1.9% between our segmentation and manual segmentation. The multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET.

Improving the efficiency and accuracy of individual tree crown delineation from high-density LiDAR data

Canopy height model (CHM) derived from LiDAR (Light Detection And Ranging) data has been commonly used to generate segments of individual tree crowns for forest inventory and sustainable management. However, branches, tree crowns, and tree clusters usually have similar shapes and overlapping sizes, which cause current individual tree crown delineation methods to work less effectively on closed canopy, deciduous or mixedwood forests. In addition, the potential of 3-dimentional (3-D) LiDAR data is not fully realized by CHM-oriented methods. In this study, a framework was proposed to take advantage of the simplicity of a CHM-oriented method, detailed vertical structures of tree crowns represented in high-density LiDAR data, and any prior knowledge of tree crowns. The efficiency and accuracy of ITC delineation can be improved. This framework consists of five steps: (1) determination of dominant crown sizes; (2) generation of initial tree segments using a multi-scale segmentation method; (3) identification of “problematic” segments; (4) determination of the number of trees based on the 3-D LiDAR points in each of the identified segments; and (5) refinement of the “problematic” segments by splitting and merging operations. The proposed framework was efficient, since the detailed examination of 3-D LiDAR points was not applied to all initial segments, but only to those needed further evaluations based on prior knowledge. It was also demonstrated to be effective based on an experiment on natural forests in Ontario, Canada. The proposed framework and specific methods yielded crown maps having a good consistency with manual and visual interpretation. The automated method correctly delineated about 74% and 72% of the tree crowns in two plots with mixedwood and deciduous trees, respectively.

Boundary-constrained multi-scale segmentation method for remote sensing images

Image segmentation is the key step of Object-Based Image Analysis (OBIA) in remote sensing. This paper proposes a Boundary-Constrained Multi-Scale Segmentation (BCMS) method. Firstly, adjacent pixels are aggregated to generate initial segmentation according to the local best region growing strategy. Then, the Region Adjacency Graph (RAG) is built based on initial segmentation. Finally, the local mutual best region merging strategy is applied on RAG to produce multi-scale segmentation results. During the region merging process, a Step-Wise Scale Parameter (SWSP) strategy is proposed to produce boundary-constrained multi-scale segmentation results. Moreover, in order to improve the accuracy of object boundaries, the property of edge strength is introduced as a merging criterion. A set of high spatial resolution remote sensing images is used in the experiment, e.g., QuickBird, WorldView, and aerial image, to evaluate the effectiveness of the proposed method. The segmentation results of BCMS are compared with those of the commercial image analysis software eCognition. The experiment shows that BCMS can produce nested multi-scale segmentations with accurate and smooth boundaries, which proves the robustness of the proposed method.

Exact Inference of Hidden Markov Tree for Image Segmentation

Image segmentation methods that exploit multiscale information about images to be estimated have been extensively studied, typically using the Hidden Markov Tree (HMT) framework. we incorporate wavelet coefficients information of the original image in the form of Hidden Markov Tree model prior for the object segmentation. In this paper, we derive a generalized closed form inference scheme to exact determine the posterior likelihood at each iteration with definite number of iteration steps. Extensive experiments show that this method performs better than many competitive multiscale image segmentation methods.

Image Segmentation Based on Contextual Label Tree for Retrieval

Image segmentation is an important constituent portion in image processing and retrieval. Based on the traditional Wavelet-domain Hidden Markov Tree (HMT) Multi-scale Segmentation method, this paper presents a Contextual Label Tree (CLT) method according to the dependency information between image blocks belong to different scales, including the relation from the father node, the neighbor nodes and the neighbor nodes of the father. This method calculates the maximal similarity using context vectors that exit on every tree node and realizes image segmentation from coarse-scale to fine-scale. Experiments show that this method is satisfied with its segmentation performance.

An Efficient Multiscale SRMMHR (Statistical Region Merging and Minimum Heterogeneity Rule) Segmentation Method for High-Resolution Remote Sensing Imagery

Multiscale segmentation is an essential step for higher level image processing in remote sensing. This paper presents a new multiscale SRMMHR segmentation method integrating the advantages of Statistical Region Merging (SRM) for initial segmentation and the Minimum Heterogeneity Rule (MHR) for object merging. The high-resolution (HR) QuickBird imageries are used to demonstrate the SRMMHR segmentation method. The SRM segmentation method not only considers spectral, shape, and scale information, but also has the ability to cope with significant noise corruption and handle occlusions. The MHR used for merging objects takes advantage of its spectral, shape, scale information, and the local and global information. Compared with the Fractal Net Evolution Approach (FNEA) that eCognition adopted and SRM methods, the results show that the proposed method wipes off small redundant objects existed in traditional SRM methods, avoids the phenomena where the big homogeneity region has lots of small similar regions existed in the FNEA method, and gets more integrated and accurate objects. Therefore, the proposed SRMMHR segmentation method is an efficient multiscale segmentation method for HR imagery.

Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation.

Segmentation of density maps obtained using cryo-electron microscopy (cryo-EM) is a challenging task, and is typically accomplished by time-intensive interactive methods. The goal of segmentation is to identify the regions inside the density map that correspond to individual components. We present a multi-scale segmentation method for accomplishing this task that requires very little user interaction. The method uses the concept of scale space, which is created by convolution of the input density map with a Gaussian filter. The latter process smoothes the density map. The standard deviation of the Gaussian filter is varied, with smaller values corresponding to finer scales and larger values to coarser scales. Each of the maps at different scales is segmented using the watershed method, which is very efficient, completely automatic, and does not require the specification of seed points. Some detail is lost in the smoothing process. A sharpening process reintroduces detail into the segmentation at the coarsest scale by using the segmentations at the finer scales. We apply the method to simulated density maps, where the exact segmentation (or ground truth) is known, and rigorously evaluate the accuracy of the resulting segmentations.

Watershed-based multiscale segmentation method for color images using automated scale selection

An automated multiscale segmentation approach for color images is presented. The scale-space stack is generated us- ing the Perona-Malik diffusion approach and the watershed algo- rithm is employed to produce the regions at each scale. A minima- linking process by downward projection is carried out over the successive scales, and a region dissimilarity measure—combining scale, contrast, and homogeneity—is subsequently estimated on the finer scale (localization scale). The dissimilarity measure is es- timated as a function of two different features, i.e., the dynamics of contours and the relative entropy of color region distributions, com- bined by means of a fuzzy-rule-based system. A region-merging process is also applied to the localization scale to produce the final regions. To validate the performance of the proposed multiscale segmentation, qualitative and quantitative results are provided in comparison to its single-scale counterpart. We also deal with the topic of localization scale selection. This stage is critical for the final segmentation results and can be used as a preprocessing step for higher level computer vision applications as well. A preliminary study of localization scale selection techniques is carried out. A scale se- lection method that originates from the evolution of the probability distribution of a region homogeneity measure across the generated scales is proposed next. The proposed algorithm is finally compared to a previously reported approach to indicate its efficiency. © 2005

A multiscale optimization approach for the dynamic contour-based boundary detection issue

We present a new multiscale approach for deformable contour optimization. The method relies on a multigrid minimization method and a coarse-to-fine relaxation algorithm. This approach consists in minimizing a cascade of optimization problems of reduced and increasing complexity instead of considering the minimization problem on the full and original configuration space. Contrary to classical multiresolution algorithms, no reduction of image is applied. The family of defined energy functions are derived from the original (full resolution) objective function, ensuring that the same function is handled at each scale and that the energy decreases at each step of the deformable contour minimization process. The efficiency and the speed of this multiscale optimization strategy is demonstrated in the difficult context of the minimization of a region-based contour energy function ensuring the boundary detection of anatomical structures in ultrasound medical imagery. In this context, the proposed multiscale segmentation method is compared to other classical region-based segmentation approaches such as Maximum Likelihood or Markov Random Field-based segmentation techniques. We also extend this multiscale segmentation strategy to active contour models using a classical edge-based likelihood approach. Finally, time and performance analysis of this approach, compared to the (commonly used) dynamic programming-based optimization procedure, is given and allows to attest the accuracy and the speed of the proposed method.